Xuanyu Hao, Dongyang Li, Xingyong Huang, Tingting Wang, Peng Wu, Lufan Shen, Kai Zhang, Siyu Sun
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引用次数: 0
Abstract
Objectives: This study aimed to explore the association between remnant cholesterol and metabolic dysfunction-associated fatty liver disease (MAFLD) in an adult population in the United States.
Methods: Data were collected from the National Health and Nutrition Examination Survey database during 2017-2020. Weighted multivariable logistic regression analyses and receiver operating characteristic (ROC) curves were used to investigate the association between remnant cholesterol and the risk of MAFLD. Subgroup and interaction analyses were performed. To further investigate the possible non-linear relationship between remnant cholesterol and MAFLD, a restricted cubic spline was used.
Results: Among the included 3633 participants, the prevalence rate of MAFLD was 34.56%. After full adjustment, higher remnant cholesterol was associated with the risk of MAFLD (odds ratio [OR], 1.04; 95% confidence interval [CI], 1.01-1.06; P = 0.02), and compared with the lowest quartile of remnant cholesterol, the highest quartile of remnant cholesterol was more likely to be associated with MAFLD (OR, 3.70; 95%CI, 2.37,5.76; P < 0.0001). A non-linear relationship between remnant cholesterol and MAFLD was found in the restricted cubic spline regression model, suggesting that the risk of MAFLD initially increased rapidly and then gradually slowed down.
Conclusion: Remnant cholesterol was identified as a potential risk factor for MAFLD, and a non-linear relationship between remnant cholesterol and the prevalence of MAFLD was detected. Large-scale, high-quality prospective studies are required to validate these findings.
期刊介绍:
Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects.
The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases.
Key areas we wish to encourage submissions from include:
-how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes;
-the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components;
-how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved;
-how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.